refactor

app.py

@@ -1,39 +1,41 @@
-import spaces
-
 import os
-import imageio
+import shutil
+import tempfile
+from functools import partial
+
+import gradio as gr
 import numpy as np
-import torch
 import rembg
+import torch
+from diffusers import DiffusionPipeline, EulerAncestralDiscreteScheduler
+from einops import rearrange
+from huggingface_hub import hf_hub_download
+from omegaconf import OmegaConf
 from PIL import Image
-from torchvision.transforms import v2
 from pytorch_lightning import seed_everything
-from omegaconf import OmegaConf
-from einops import rearrange, repeat
+from torchvision.transforms import v2
 from tqdm import tqdm
-from diffusers import DiffusionPipeline, EulerAncestralDiscreteScheduler
 
+from src.utils.camera_util import FOV_to_intrinsics, get_circular_camera_poses, get_zero123plus_input_cameras
+from src.utils.infer_util import images_to_video, remove_background, resize_foreground
+from src.utils.mesh_util import save_glb, save_obj
 from src.utils.train_util import instantiate_from_config
-from src.utils.camera_util import (
-    FOV_to_intrinsics, 
-    get_zero123plus_input_cameras,
-    get_circular_camera_poses,
-)
-from src.utils.mesh_util import save_obj, save_glb
-from src.utils.infer_util import remove_background, resize_foreground, images_to_video
 
-import tempfile
-from functools import partial
 
-from huggingface_hub import hf_hub_download
+def find_cuda():
+    cuda_home = os.environ.get('CUDA_HOME') or os.environ.get('CUDA_PATH')
+    if cuda_home and os.path.exists(cuda_home):
+        return cuda_home
 
-import gradio as gr
+    nvcc_path = shutil.which('nvcc')
+    if nvcc_path:
+        cuda_path = os.path.dirname(os.path.dirname(nvcc_path))
+        return cuda_path
+
+    return None
 
 
 def get_render_cameras(batch_size=1, M=120, radius=2.5, elevation=10.0, is_flexicubes=False):
-    """
-    Get the rendering camera parameters.
-    """
     c2ws = get_circular_camera_poses(M=M, radius=radius, elevation=elevation)
     if is_flexicubes:
         cameras = torch.linalg.inv(c2ws)
@@ -46,89 +48,42 @@ def get_render_cameras(batch_size=1, M=120, radius=2.5, elevation=10.0, is_flexi
     return cameras
 
 
-def images_to_video(images, output_path, fps=30):
-    # images: (N, C, H, W)
-    os.makedirs(os.path.dirname(output_path), exist_ok=True)
-    frames = []
-    for i in range(images.shape[0]):
-        frame = (images[i].permute(1, 2, 0).cpu().numpy() * 255).astype(np.uint8).clip(0, 255)
-        assert frame.shape[0] == images.shape[2] and frame.shape[1] == images.shape[3], \
-            f"Frame shape mismatch: {frame.shape} vs {images.shape}"
-        assert frame.min() >= 0 and frame.max() <= 255, \
-            f"Frame value out of range: {frame.min()} ~ {frame.max()}"
-        frames.append(frame)
-    imageio.mimwrite(output_path, np.stack(frames), fps=fps, codec='h264')
-
-
-###############################################################################
-# Configuration.
-###############################################################################
-
-import shutil
-
-def find_cuda():
-    # Check if CUDA_HOME or CUDA_PATH environment variables are set
-    cuda_home = os.environ.get('CUDA_HOME') or os.environ.get('CUDA_PATH')
-
-    if cuda_home and os.path.exists(cuda_home):
-        return cuda_home
-
-    # Search for the nvcc executable in the system's PATH
-    nvcc_path = shutil.which('nvcc')
-
-    if nvcc_path:
-        # Remove the 'bin/nvcc' part to get the CUDA installation path
-        cuda_path = os.path.dirname(os.path.dirname(nvcc_path))
-        return cuda_path
-
-    return None
+def load_models(config_path):
+    config = OmegaConf.load(config_path)
+    config_name = os.path.basename(config_path).replace('.yaml', '')
+    model_config = config.model_config
+    infer_config = config.infer_config
 
-cuda_path = find_cuda()
+    is_flexicubes = config_name.startswith('instant-mesh')
 
-if cuda_path:
-    print(f"CUDA installation found at: {cuda_path}")
-else:
-    print("CUDA installation not found")
+    device = torch.device('cuda')
 
-config_path = 'configs/instant-mesh-large.yaml'
-config = OmegaConf.load(config_path)
-config_name = os.path.basename(config_path).replace('.yaml', '')
-model_config = config.model_config
-infer_config = config.infer_config
-
-IS_FLEXICUBES = True if config_name.startswith('instant-mesh') else False
-
-device = torch.device('cuda')
-
-# load diffusion model
-print('Loading diffusion model ...')
-pipeline = DiffusionPipeline.from_pretrained(
-    "sudo-ai/zero123plus-v1.2", 
-    custom_pipeline="zero123plus",
-    torch_dtype=torch.float16,
-)
-pipeline.scheduler = EulerAncestralDiscreteScheduler.from_config(
-    pipeline.scheduler.config, timestep_spacing='trailing'
-)
+    pipeline = DiffusionPipeline.from_pretrained(
+        "sudo-ai/zero123plus-v1.2",
+        custom_pipeline="zero123plus",
+        torch_dtype=torch.float16,
+    )
+    pipeline.scheduler = EulerAncestralDiscreteScheduler.from_config(
+        pipeline.scheduler.config, timestep_spacing='trailing'
+    )
 
-# load custom white-background UNet
-unet_ckpt_path = hf_hub_download(repo_id="TencentARC/InstantMesh", filename="diffusion_pytorch_model.bin", repo_type="model")
-state_dict = torch.load(unet_ckpt_path, map_location='cpu')
-pipeline.unet.load_state_dict(state_dict, strict=True)
+    unet_ckpt_path = hf_hub_download(
+        repo_id="TencentARC/InstantMesh", filename="diffusion_pytorch_model.bin", repo_type="model")
+    state_dict = torch.load(unet_ckpt_path, map_location='cpu')
+    pipeline.unet.load_state_dict(state_dict, strict=True)
 
-pipeline = pipeline.to(device)
+    pipeline = pipeline.to(device)
 
-# load reconstruction model
-print('Loading reconstruction model ...')
-model_ckpt_path = hf_hub_download(repo_id="TencentARC/InstantMesh", filename="instant_mesh_large.ckpt", repo_type="model")
-model = instantiate_from_config(model_config)
-state_dict = torch.load(model_ckpt_path, map_location='cpu')['state_dict']
-state_dict = {k[14:]: v for k, v in state_dict.items() if k.startswith('lrm_generator.') and 'source_camera' not in k}
-model.load_state_dict(state_dict, strict=True)
+    model_ckpt_path = hf_hub_download(
+        repo_id="TencentARC/InstantMesh", filename="instant_mesh_large.ckpt", repo_type="model")
+    model = instantiate_from_config(model_config)
+    state_dict = torch.load(model_ckpt_path, map_location='cpu')['state_dict']
+    state_dict = {k[14:]: v for k, v in state_dict.items() if k.startswith('lrm_generator.') and 'source_camera' not in k}
+    model.load_state_dict(state_dict, strict=True)
 
-model = model.to(device)
+    model = model.to(device)
 
-print('Loading Finished!')
+    return pipeline, model, is_flexicubes, infer_config
 
 
 def check_input_image(input_image):
@@ -137,7 +92,6 @@ def check_input_image(input_image):
 
 
 def preprocess(input_image, do_remove_background):
-
     rembg_session = rembg.new_session() if do_remove_background else None
 
     if do_remove_background:
@@ -147,19 +101,16 @@ def preprocess(input_image, do_remove_background):
     return input_image
 
 
-@spaces.GPU
-def generate_mvs(input_image, sample_steps, sample_seed):
-
+def generate_mvs(input_image, sample_steps, sample_seed, pipeline):
     seed_everything(sample_seed)
-    
-    # sampling
+
     z123_image = pipeline(
-        input_image, 
+        input_image,
         num_inference_steps=sample_steps
     ).images[0]
 
     show_image = np.asarray(z123_image, dtype=np.uint8)
-    show_image = torch.from_numpy(show_image)     # (960, 640, 3)
+    show_image = torch.from_numpy(show_image)
     show_image = rearrange(show_image, '(n h) (m w) c -> (n m) h w c', n=3, m=2)
     show_image = rearrange(show_image, '(n m) h w c -> (n h) (m w) c', n=2, m=3)
     show_image = Image.fromarray(show_image.numpy())
@@ -167,224 +118,147 @@ def generate_mvs(input_image, sample_steps, sample_seed):
     return z123_image, show_image
 
 
-@spaces.GPU
-def make3d(images):
+def make3d(images, model, is_flexicubes, infer_config):
+    device = torch.device('cuda')
 
-    global model
-    if IS_FLEXICUBES:
+    if is_flexicubes:
         model.init_flexicubes_geometry(device, use_renderer=False)
     model = model.eval()
 
     images = np.asarray(images, dtype=np.float32) / 255.0
-    images = torch.from_numpy(images).permute(2, 0, 1).contiguous().float()     # (3, 960, 640)
-    images = rearrange(images, 'c (n h) (m w) -> (n m) c h w', n=3, m=2)        # (6, 3, 320, 320)
+    images = torch.from_numpy(images).permute(2, 0, 1).contiguous().float()
+    images = rearrange(images, 'c (n h) (m w) -> (n m) c h w', n=3, m=2)
 
     input_cameras = get_zero123plus_input_cameras(batch_size=1, radius=4.0).to(device)
-    render_cameras = get_render_cameras(batch_size=1, radius=2.5, is_flexicubes=IS_FLEXICUBES).to(device)
+    render_cameras = get_render_cameras(batch_size=1, radius=2.5, is_flexicubes=is_flexicubes).to(device)
 
     images = images.unsqueeze(0).to(device)
     images = v2.functional.resize(images, (320, 320), interpolation=3, antialias=True).clamp(0, 1)
 
     mesh_fpath = tempfile.NamedTemporaryFile(suffix=f".obj", delete=False).name
-    print(mesh_fpath)
     mesh_basename = os.path.basename(mesh_fpath).split('.')[0]
     mesh_dirname = os.path.dirname(mesh_fpath)
-    video_fpath = os.path.join(mesh_dirname, f"{mesh_basename}.mp4")
     mesh_glb_fpath = os.path.join(mesh_dirname, f"{mesh_basename}.glb")
 
     with torch.no_grad():
-        # get triplane
         planes = model.forward_planes(images, input_cameras)
-
-        # # get video
-        # chunk_size = 20 if IS_FLEXICUBES else 1
-        # render_size = 384
-        
-        # frames = []
-        # for i in tqdm(range(0, render_cameras.shape[1], chunk_size)):
-        #     if IS_FLEXICUBES:
-        #         frame = model.forward_geometry(
-        #             planes,
-        #             render_cameras[:, i:i+chunk_size],
-        #             render_size=render_size,
-        #         )['img']
-        #     else:
-        #         frame = model.synthesizer(
-        #             planes,
-        #             cameras=render_cameras[:, i:i+chunk_size],
-        #             render_size=render_size,
-        #         )['images_rgb']
-        #     frames.append(frame)
-        # frames = torch.cat(frames, dim=1)
-
-        # images_to_video(
-        #     frames[0],
-        #     video_fpath,
-        #     fps=30,
-        # )
-
-        # print(f"Video saved to {video_fpath}")
-
-        # get mesh
-        mesh_out = model.extract_mesh(
-            planes,
-            use_texture_map=False,
-            **infer_config,
-        )
+        mesh_out = model.extract_mesh(planes, use_texture_map=False, **infer_config)
 
         vertices, faces, vertex_colors = mesh_out
         vertices = vertices[:, [1, 2, 0]]
-        
+
         save_glb(vertices, faces, vertex_colors, mesh_glb_fpath)
         save_obj(vertices, faces, vertex_colors, mesh_fpath)
-        
-        print(f"Mesh saved to {mesh_fpath}")
 
     return mesh_fpath, mesh_glb_fpath
 
 
-_HEADER_ = '''
-<h2><b>Official 🤗 Gradio Demo</b></h2><h2><a href='https://github.com/TencentARC/InstantMesh' target='_blank'><b>InstantMesh: Efficient 3D Mesh Generation from a Single Image with Sparse-view Large Reconstruction Models</b></a></h2>
-
-**InstantMesh** is a feed-forward framework for efficient 3D mesh generation from a single image based on the LRM/Instant3D architecture.
-
-Code: <a href='https://github.com/TencentARC/InstantMesh' target='_blank'>GitHub</a>. Techenical report: <a href='https://arxiv.org/abs/2404.07191' target='_blank'>ArXiv</a>.
-
-❗️❗️❗️**Important Notes:**
-- Our demo can export a .obj mesh with vertex colors or a .glb mesh now. If you prefer to export a .obj mesh with a **texture map**, please refer to our <a href='https://github.com/TencentARC/InstantMesh?tab=readme-ov-file#running-with-command-line' target='_blank'>Github Repo</a>.
-- The 3D mesh generation results highly depend on the quality of generated multi-view images. Please try a different **seed value** if the result is unsatisfying (Default: 42).
-'''
-
-_CITE_ = r"""
-If InstantMesh is helpful, please help to ⭐ the <a href='https://github.com/TencentARC/InstantMesh' target='_blank'>Github Repo</a>. Thanks! [![GitHub Stars](https://img.shields.io/github/stars/TencentARC/InstantMesh?style=social)](https://github.com/TencentARC/InstantMesh)
----
-📝 **Citation**
-
-If you find our work useful for your research or applications, please cite using this bibtex:
-```bibtex
-@article{xu2024instantmesh,
-  title={InstantMesh: Efficient 3D Mesh Generation from a Single Image with Sparse-view Large Reconstruction Models},
-  author={Xu, Jiale and Cheng, Weihao and Gao, Yiming and Wang, Xintao and Gao, Shenghua and Shan, Ying},
-  journal={arXiv preprint arXiv:2404.07191},
-  year={2024}
-}
-```
-
-📋 **License**
-
-Apache-2.0 LICENSE. Please refer to the [LICENSE file](https://huggingface.co/spaces/TencentARC/InstantMesh/blob/main/LICENSE) for details.
-
-📧 **Contact**
-
-If you have any questions, feel free to open a discussion or contact us at <b>[email protected]</b>.
-"""
-
-
-with gr.Blocks() as demo:
-    gr.Markdown(_HEADER_)
-    with gr.Row(variant="panel"):
-        with gr.Column():
-            with gr.Row():
-                input_image = gr.Image(
-                    label="Input Image",
-                    image_mode="RGBA",
-                    sources="upload",
-                    #width=256,
-                    #height=256,
-                    type="pil",
-                    elem_id="content_image",
-                )
-                processed_image = gr.Image(
-                    label="Processed Image", 
-                    image_mode="RGBA", 
-                    #width=256,
-                    #height=256,
-                    type="pil", 
-                    interactive=False
-                )
-            with gr.Row():
-                with gr.Group():
-                    do_remove_background = gr.Checkbox(
-                        label="Remove Background", value=True
-                    )
-                    sample_seed = gr.Number(value=42, label="Seed Value", precision=0)
-
-                    sample_steps = gr.Slider(
-                        label="Sample Steps",
-                        minimum=30,
-                        maximum=75,
-                        value=75,
-                        step=5
+def launch_demo(config_path):
+    cuda_path = find_cuda()
+    if cuda_path:
+        print(f"CUDA installation found at: {cuda_path}")
+    else:
+        print("CUDA installation not found")
+
+    pipeline, model, is_flexicubes, infer_config = load_models(config_path)
+
+    with gr.Blocks() as demo:
+        with gr.Row(variant="panel"):
+            with gr.Column():
+                with gr.Row():
+                    input_image = gr.Image(
+                        label="Input Image",
+                        image_mode="RGBA",
+                        sources="upload",
+                        type="pil",
+                        elem_id="content_image",
                     )
-
-            with gr.Row():
-                submit = gr.Button("Generate", elem_id="generate", variant="primary")
-
-            with gr.Row(variant="panel"):
-                gr.Examples(
-                    examples=[
-                        os.path.join("examples", img_name) for img_name in sorted(os.listdir("examples"))
-                    ],
-                    inputs=[input_image],
-                    label="Examples",
-                    cache_examples=False,
-                    examples_per_page=16
-                )
-
-        with gr.Column():
-
-            with gr.Row():
-
-                with gr.Column():
-                    mv_show_images = gr.Image(
-                        label="Generated Multi-views",
+                    processed_image = gr.Image(
+                        label="Processed Image",
+                        image_mode="RGBA",
                         type="pil",
-                        width=379,
                         interactive=False
                     )
-
-                # with gr.Column():
-                #     output_video = gr.Video(
-                #         label="video", format="mp4",
-                #         width=379,
-                #         autoplay=True,
-                #         interactive=False
-                #     )
-
-            with gr.Row():
-                with gr.Tab("OBJ"):
-                    output_model_obj = gr.Model3D(
-                        label="Output Model (OBJ Format)",
-                        interactive=False,
+                with gr.Row():
+                    with gr.Group():
+                        do_remove_background = gr.Checkbox(
+                            label="Remove Background", value=True
+                        )
+                        sample_seed = gr.Number(
+                            value=42, label="Seed Value", precision=0)
+
+                        sample_steps = gr.Slider(
+                            label="Sample Steps",
+                            minimum=30,
+                            maximum=75,
+                            value=75,
+                            step=5
+                        )
+
+                with gr.Row():
+                    submit = gr.Button(
+                        "Generate", elem_id="generate", variant="primary")
+
+                with gr.Row(variant="panel"):
+                    gr.Examples(
+                        examples=[
+                            os.path.join("examples", img_name) for img_name in sorted(os.listdir("examples"))
+                        ],
+                        inputs=[input_image],
+                        label="Examples",
+                        cache_examples=False,
+                        examples_per_page=16
                     )
-                    gr.Markdown("Note: Downloaded .obj model will be flipped. Export .glb instead or manually flip it before usage.")
-                with gr.Tab("GLB"):
-                    output_model_glb = gr.Model3D(
-                        label="Output Model (GLB Format)",
-                        interactive=False,
-                    )
-                    gr.Markdown("Note: The model shown here has a darker appearance. Download to get correct results.")
-
-            with gr.Row():
-                gr.Markdown('''Try a different <b>seed value</b> if the result is unsatisfying (Default: 42).''')
-
-    gr.Markdown(_CITE_)
-
-    mv_images = gr.State()
-
-    submit.click(fn=check_input_image, inputs=[input_image]).success(
-        fn=preprocess,
-        inputs=[input_image, do_remove_background],
-        outputs=[processed_image],
-    ).success(
-        fn=generate_mvs,
-        inputs=[processed_image, sample_steps, sample_seed],
-        outputs=[mv_images, mv_show_images]
-        
-    ).success(
-        fn=make3d,
-        inputs=[mv_images],
-        outputs=[output_model_obj, output_model_glb]
-    )
 
-demo.launch()
+            with gr.Column():
+                with gr.Row():
+                    with gr.Column():
+                        mv_show_images = gr.Image(
+                            label="Generated Multi-views",
+                            type="pil",
+                            width=379,
+                            interactive=False
+                        )
+
+                with gr.Row():
+                    with gr.Tab("OBJ"):
+                        output_model_obj = gr.Model3D(
+                            label="Output Model (OBJ Format)",
+                            interactive=False,
+                        )
+                        gr.Markdown(
+                            "Note: Downloaded .obj model will be flipped. Export .glb instead or manually flip it before usage.")
+                    with gr.Tab("GLB"):
+                        output_model_glb = gr.Model3D(
+                            label="Output Model (GLB Format)",
+                            interactive=False,
+                        )
+                        gr.Markdown(
+                            "Note: The model shown here has a darker appearance. Download to get correct results.")
+
+                with gr.Row():
+                    gr.Markdown(
+                        '''Try a different <b>seed value</b> if the result is unsatisfying (Default: 42).''')
+
+        mv_images = gr.State()
+
+        submit.click(fn=check_input_image, inputs=[input_image]).success(
+            fn=preprocess,
+            inputs=[input_image, do_remove_background],
+            outputs=[processed_image],
+        ).success(
+            fn=generate_mvs,
+            inputs=[processed_image, sample_steps, sample_seed, pipeline],
+            outputs=[mv_images, mv_show_images]
+        ).success(
+            fn=make3d,
+            inputs=[mv_images, model, is_flexicubes, infer_config],
+            outputs=[output_model_obj, output_model_glb]
+        )
+
+    demo.launch()
+
+
+if __name__ == "__main__":
+    config_path = 'configs/instant-mesh-large.yaml'
+    launch_demo(config_path)